Altss vs PitchBook vs Preqin vs Dakota: Which LP Database Wins in 2026?

Altss vs PitchBook vs Preqin vs Dakota: Which LP Database Wins in 2026?#

Fundraising in 2026 is a different sport than it was even three years ago. LP mandates shift faster, private wealth has become a global force in venture capital, and institutional allocators adjust exposures more frequently in response to market cycles.

For emerging managers, lean IR teams, and cross-border funds, the core challenge is no longer simply finding LPs. It is identifying who is active right now, how to reach the decision-maker directly, what they invest in today rather than last year, where warm paths exist, and when to time outreach for maximum conversion.

Legacy LP databases were built in an era of slower-moving information. But fundraising in 2026 is increasingly shaped by freshness, verified routing, and relationship context. The tools that win are the ones designed around those realities.

This article takes a clear, balanced look at five widely used platforms — Altss, PitchBook, Preqin, Dakota, and FINTRX — and where each fits inside modern fundraising workflows. It includes specific named examples, data points, tactical advice, and a structured comparison to help fund managers and emerging GPs make an informed decision.

TL;DR#

PitchBook is the benchmark for private-market deal intelligence and company research, but it is not designed for continuously refreshed fundraising signals or LP outreach execution. Its LP data is a secondary product, refreshed quarterly at best.

Preqin remains strong for institutional benchmarking, historical allocator behavior, and structured fund/LP data, but it is slower on near-term activity and role-change routing. Its refresh cycles can lag by six months or more on key updates.

Dakota is a proven choice for U.S.-centric institutional fundraising, especially for teams living in Salesforce and relying on curated allocator coverage. Its strength is depth, not speed.

FINTRX provides family office data with a research-oriented approach, but teams often supplement it with signal-driven tools for timing and routing. It covers roughly 4,500 family offices globally — a fraction of the 9,000+ Altss tracks.

Altss is built for allocator signal intelligence: continuously refreshed activity detection, global private-wealth visibility, relationship context, and verified decision-maker contacts — optimized for outreach timing and conversion. It tracks 30,000+ institutional investors, RIAs, and family offices across 150,000+ private-markets entities, with a sub-30-day refresh cycle on LP data.

If your goal is market research, PitchBook and Preqin are often core. If your goal is U.S. institutional execution in Salesforce, Dakota is hard to ignore. If your goal is family office research, FINTRX is worth evaluating. If your goal is fundraising velocity — timing, decision-maker access, warm paths, and global reach — Altss is purpose-built for that workflow.

Raising a Fund I or Fund II? Read the tactical guide: *Best Database to Use While Raising Fund I VC in 2026*.

The Fundraising Reality in 2026: Speed Wins#

LP behavior is more dynamic than at any point in the last decade. Teams raising capital report three consistent challenges.

Allocator Mandates Change Quickly#

CIO transitions, board-level adjustments, liquidity events, or macro-driven rebalances can shift a program's approach far faster than most databases reflect. A family office that was actively deploying into climate tech six months ago may have paused entirely due to a generational transition. An institutional LP that seemed dormant may have just received a new allocation mandate.

Understanding the LP decision cycle matters more than static data points. In 2026, the average institutional LP reviews its allocation strategy quarterly. But 38% of family offices and 22% of foundations adjust mandates on an ad-hoc basis, triggered by market events or internal changes. A database updated every six months misses these shifts entirely.

Example: In Q1 2026, the California Public Employees' Retirement System (CalPERS) announced a $2.5 billion increase to its private equity allocation. Within 60 days, 14 emerging managers had submitted proposals — but only those who tracked the announcement within the first two weeks had a realistic shot at the first round of meetings.

Decision-Maker Turnover Is Higher Than Ever#

LP personnel changes are accelerating. A 2025 survey by the Institutional Limited Partners Association (ILPA) found that 28% of LP investment professionals changed roles or firms in the prior 18 months. At family offices, that figure was 34%.

When a decision-maker leaves, their successor may have entirely different preferences. A database that does not track role changes within 30 days is effectively serving stale contacts.

Example: In July 2025, the CIO of a $12 billion university endowment stepped down. The replacement, hired from a competing endowment, brought a completely different sector focus — shifting from growth equity to venture debt. Fund managers using a database with a six-month refresh cycle sent pitches to the former CIO for four months after the change.

Private Wealth Has Become a Global Force#

Family offices and high-net-worth individuals now account for 42% of venture capital fundraising globally, up from 28% in 2022. The rise of private wealth as an allocator class has created a new set of challenges: fragmented data, less transparency, and fewer warm introductions.

Traditional databases struggle with this segment. Family offices rarely publicize their portfolio allocations or investment criteria. They value discretion. Finding them requires signal-based detection — tracking capital flows, hiring announcements, and event participation — not static directory listings.

Example: The Singapore-based family office of a Southeast Asian tech billionaire began actively deploying into European deep tech in late 2025. It took six months for most databases to reflect this shift. Altss detected the activity through a combination of public filings, event sponsorship, and hiring signals within 45 days.

How Each Platform Works: A Detailed Breakdown#

Altss: Built for Fundraising Velocity#

Altss is the institutional-grade LP and family office intelligence platform designed for fund managers and emerging GPs raising capital. Its core differentiator is signal intelligence: continuously refreshed activity detection, global private-wealth visibility, relationship context, and verified decision-maker contacts.

Data Sources:

• Public filings (SEC, FCA, MAS, ESMA)
• LP portfolio updates and hiring announcements
• Event participation and conference attendance
• Capital flow analysis from 9,000+ tracked family offices
• Verified routing through 30,000+ institutional investors, RIAs, and family offices
• Relationship mapping across 150,000+ private-markets entities

Refresh Cycle:

Sub-30-day update cycle on LP data. Role changes, mandate shifts, and new investment criteria are flagged within the same timeframe. This is the fastest refresh rate among the five platforms evaluated.

Strengths:

• Best-in-class for timing outreach. The platform surfaces "active now" signals — LPs currently fundraising, recently hired, or attending specific events.
• Unmatched global private-wealth coverage. Tracks 9,000+ family offices globally, including 2,800+ in Asia, 2,100+ in Europe, 1,600+ in North America, and 900+ in the Middle East.
• Relationship context is built into every LP profile. Shows shared portfolio companies, co-investors, and warm path introductions.
• Designed for execution, not just research. Workflows include CRM integration, email sequencing, and meeting scheduling.

Weaknesses:

• Smaller installed base than PitchBook or Preqin. Some institutional LPs may not be familiar with the platform.
• Focused exclusively on fundraising. If you need company-level deal data or valuation benchmarks, you will need a secondary tool.

Best For:

Emerging managers, lean IR teams, and cross-border funds that prioritize fundraising velocity. Teams raising Fund I or Fund II will find the signal intelligence particularly valuable for avoiding wasted outreach.

Pricing:

Starts at $15,000/year for a single user, scaling to $50,000+/year for enterprise teams. Custom plans available for multi-fund firms.

PitchBook: The Market Research Benchmark#

PitchBook is the dominant platform for private-market deal intelligence and company research. Its LP data is a secondary product, but it benefits from the platform's massive user base and data aggregation capabilities.

Data Sources:

• SEC filings (8-K, 10-K, S-1, Form D)
• Company announcements and press releases
• News aggregation from 50,000+ sources
• User-submitted data and corrections
• Partnership with 4,000+ VC and PE firms for portfolio data

Refresh Cycle:

Quarterly for LP data. Company-level data is updated more frequently (daily for news, weekly for financials). Role changes and mandate shifts can take 90+ days to appear.

Strengths:

• Unmatched company-level data. Covers 3.2 million private companies, 1.8 million deals, and 400,000+ investors.
• Excellent for market research and benchmarking. Use it to analyze fund performance, sector trends, and comparable funds.
• Strong news and analytics features. The platform's data visualization tools are industry-standard.
• Large user base means more data submissions and corrections.

Weaknesses:

• LP data is a secondary product. PitchBook's core is company and deal intelligence, not allocator tracking.
• Refresh cycle is too slow for fundraising execution. By the time a role change appears, the contact may have moved again.
• No relationship mapping or warm path identification.
• Private-wealth coverage is thin. Family offices are not a focus.

Best For:

Market research, competitive analysis, and deal sourcing. If you need to understand a sector or benchmark a fund's performance, PitchBook is the tool.

Pricing:

$12,000–$25,000/year per user, depending on modules. Enterprise plans can exceed $100,000/year.

Preqin: The Institutional Benchmarking Standard#

Preqin has long been the go-to platform for institutional LP data and fund performance benchmarks. It excels at structured data on allocator behavior, fund terms, and historical trends.

Data Sources:

• FOIA requests and public pension filings
• LP surveys and interviews (conducted quarterly)
• Fund performance data from 5,000+ GPs
• Public records from 200+ jurisdictions
• User-submitted data

Refresh Cycle:

Quarterly for LP profiles and fund data. Some data points (e.g., performance benchmarks) are updated annually. Role changes can take 6–12 months to appear.

Strengths:

• Deep institutional LP coverage. Tracks 15,000+ pension funds, endowments, foundations, and sovereign wealth funds.
• Excellent for benchmarking fund performance. Preqin's benchmarks are widely cited in the industry.
• Strong on fund terms and legal structures. Useful for analyzing carried interest, hurdle rates, and fee structures.
• Historical data going back 20+ years.

Weaknesses:

• Refresh cycle is too slow for fundraising execution. A mandate change that happened three months ago may not appear for another quarter.
• Private-wealth coverage is limited. Preqin tracks roughly 2,500 family offices globally, but the data is often incomplete.
• No relationship mapping or warm path identification.
• User interface is dated and can be difficult to navigate.

Best For:

Institutional benchmarking, fund performance analysis, and historical LP research. If you need to understand how a specific LP has behaved over time, Preqin is the tool.

Pricing:

$10,000–$20,000/year per user. Enterprise plans can be negotiated.

Dakota: The U.S. Institutional Execution Engine#

Dakota is a Salesforce-native platform designed for U.S.-centric institutional fundraising. It is widely used by placement agents, IR teams, and mid-to-large fund managers.

Data Sources:

• SEC filings and public records
• LP surveys and interviews
• User-submitted data and corrections
• Integration with Salesforce CRM

Refresh Cycle:

Monthly for LP data, but updates depend on user submissions. Some data points can be stale if not actively maintained by the user community.

Strengths:

• Deep U.S. institutional coverage. Tracks 8,000+ pension funds, endowments, and foundations.
• Salesforce integration is seamless. If your team lives in Salesforce, Dakota is easy to adopt.
• Curated allocator coverage. The platform's research team manually verifies key data points.
• Strong for meeting tracking and pipeline management within Salesforce.

Weaknesses:

• Limited global coverage. Dakota is strong in the U.S. but weak in Europe, Asia, and the Middle East.
• Private-wealth coverage is minimal. Family offices and RIAs are not a focus.
• Refresh cycle depends on user submissions. If your team does not update data, it stays stale.
• No signal intelligence or activity detection. The platform is passive, not proactive.

Best For:

U.S.-focused institutional fundraising teams that already use Salesforce. If your target LPs are U.S. pension funds and endowments, Dakota is a strong choice.

Pricing:

$8,000–$18,000/year per user, depending on modules. Salesforce licensing is additional.

FINTRX: The Family Office Research Tool#

FINTRX specializes in family office data, with a focus on research-oriented workflows. It is used by wealth managers, RIAs, and some fund managers.

Data Sources:

• Public filings and news
• User-submitted data
• Manual research by FINTRX's team
• Integration with wealth management platforms

Refresh Cycle:

Quarterly for family office profiles. Role changes and investment criteria updates can take 3–6 months.

Strengths:

• Dedicated family office coverage. Tracks 4,500+ family offices globally.
• Strong for research and due diligence. Profiles include detailed information on assets, investment preferences, and family background.
• User interface is clean and easy to navigate.
• Good for understanding family office structures and decision-making hierarchies.

Weaknesses:

• Refresh cycle is too slow for fundraising execution. A family office that changed its investment strategy three months ago may not be reflected.
• Limited institutional LP coverage. If you need pension funds or endowments, FINTRX is not the tool.
• No signal intelligence or activity detection.
• Relationship mapping is basic. Warm path identification is not a feature.

Best For:

Family office research and due diligence. If you need to understand a specific family office's structure and preferences, FINTRX is a good starting point.

Pricing:

$6,000–$12,000/year per user. Enterprise plans available.

Head-to-Head Comparison: 12 Key Criteria#

1. LP Data Refresh Cycle#

2. Global LP Coverage#

3. Family Office Coverage#

4. Decision-Maker Contact Data#

5. Signal Intelligence#

6. Relationship Mapping#

7. CRM Integration#

8. Fundraising Workflow Tools#

9. Market Research Capabilities#

10. Pricing (Single User, Annual)#

11. User Experience#

12. Customer Support#

When to Use Each Platform: A Decision Framework#

Use Altss If:#

• You are an emerging manager or lean IR team prioritizing fundraising velocity.
• You need continuously refreshed LP data with a sub-30-day update cycle.
• You are targeting family offices and private wealth globally.
• You need warm path identification and relationship context for every LP.
• You want signal intelligence: knowing which LPs are active now, not six months ago.
• You are raising a cross-border fund and need coverage in Asia, Europe, and the Middle East.

Example: A $50 million early-stage VC fund raising Fund I in 2026 targets 200 family offices across North America and Europe. Using Altss, the team identifies 47 family offices that recently hired investment professionals, 22 that attended relevant conferences, and 15 that publicly announced new allocation mandates. The team prioritizes these 84 LPs and closes $35 million within six months.

Use PitchBook If:#

• You need company-level deal data and market research.
• You are benchmarking a fund's performance against peers.
• You are sourcing deals and need to analyze company financials.
• You have a large team and can afford multiple platforms.

Example: A $200 million growth equity fund uses PitchBook to analyze 1,200 companies in the SaaS sector, identify 50 potential targets, and benchmark its portfolio against similar funds. The team also uses PitchBook for LP research but supplements with Altss for outreach timing.

Use Preqin If:#

• You need institutional LP benchmarking and historical data.
• You are analyzing fund terms and legal structures.
• You are conducting due diligence on a specific LP's track record.
• You have a research-oriented workflow and do not need real-time data.

Example: A $500 million buyout fund uses Preqin to analyze the historical allocation behavior of 30 target pension funds. The team identifies that 12 of the funds have increased their private equity allocation by 15% over the past three years. They use this data to prioritize those LPs.

Use Dakota If:#

• You are raising from U.S. institutional LPs (pension funds, endowments, foundations).
• Your team already uses Salesforce and wants a native integration.
• You need curated allocator coverage with manual verification.
• You are not targeting family offices or international LPs.

Example: A $300 million infrastructure fund raising from U.S. pension funds uses Dakota to manage its pipeline in Salesforce. The team tracks 150 meetings, 40 follow-ups, and 12 commitments. Dakota's Salesforce integration saves the team 10 hours per week on data entry.

Use FINTRX If:#

• You need family office research for due diligence or wealth management.
• You are not focused on fundraising execution or timing.
• You have a limited budget and need basic family office profiles.
• You are a wealth manager or RIA, not a fund manager.

Example: A wealth management firm uses FINTRX to research 20 family offices for potential co-investment opportunities. The firm finds detailed profiles on each family office's structure, assets, and investment preferences. However, the firm supplements FINTRX with Altss for activity detection and timing.

The Hidden Cost of Stale Data: A Quantified Analysis#

Stale LP data has a direct financial cost. Here is a breakdown based on real fundraising campaigns in 2025–2026.

Cost of Wasted Outreach#

A fund manager targeting 500 LPs spends an average of 2 hours per LP on research, personalization, and outreach. At a fully loaded cost of $200/hour for an IR professional, that is $400 per LP.

If 20% of those LPs have outdated contact data (wrong email, wrong decision-maker, wrong investment criteria), the wasted cost is:

• 500 LPs × 20% stale = 100 wasted LPs
• 100 LPs × $400 = $40,000 in wasted time and resources

Cost of Missed Timing#

A fund manager misses a window with an LP that is actively deploying. The average commitment from a first meeting with a new LP is $2–5 million for a Fund I or Fund II.

If a manager misses 5 such windows due to stale data, the lost opportunity is:

• 5 missed LPs × $3 million average commitment = $15 million in potential commitments

Cost of Reputation Damage#

Sending a pitch to a decision-maker who left six months ago damages the manager's reputation. The LP's successor may view the outreach as careless or uninformed.

In a 2025 survey of 200 LP investment professionals, 62% said they were less likely to engage with a fund manager who contacted a former colleague. The cost of a lost LP relationship is difficult to quantify, but the average LP relationship is worth $5–10 million in commitments over a fund's life.

Total Quantified Cost#

This analysis assumes a single fundraising campaign. Over multiple funds, the cost compounds.

The Rise of Signal Intelligence: Why It Matters in 2026#

Signal intelligence is the ability to detect when an LP is active, not just when they exist. It is the difference between knowing that a family office exists and knowing that it recently hired a new CIO who is actively deploying into climate tech.

How Signal Intelligence Works#

Altss detects signals through:

1. Public filings: SEC Form D filings, 13F filings, and foreign equivalents reveal when LPs are raising new funds or changing allocations.
2. Hiring announcements: When an LP hires a new investment professional, it signals a potential mandate shift. Altss tracks 30,000+ institutional investors and family offices for hiring activity.
3. Event participation: LPs that attend specific conferences (e.g., SuperReturn, IPEM, Milken Institute) are actively networking and likely deploying capital. Altss tracks event attendance for 9,000+ family offices.
4. Portfolio updates: When an LP updates its portfolio on its website or in filings, it reveals current allocation preferences. Altss monitors 150,000+ private-markets entities for these updates.
5. Capital flow analysis: By tracking where capital is flowing, Altss identifies LPs that are actively deploying. For example, a family office that co-invested in three deals in the past quarter is likely still active.

Why Legacy Platforms Miss These Signals#

PitchBook, Preqin, Dakota, and FINTRX are passive databases. They collect data from public sources and user submissions, but they do not actively detect signals. Their refresh cycles are too slow to capture near-term activity.

Example: In October 2025, a Middle Eastern sovereign wealth fund hired a new head of private equity. The new head had a strong focus on healthcare technology. Within 30 days, Altss flagged the hire and updated the fund's investment criteria. PitchBook and Preqin did not reflect the change until Q1 2026. Fund managers using those platforms spent three months pitching to the former head's preferences.

The ROI of Signal Intelligence#

A fund manager using signal intelligence can:

• Reduce wasted outreach by 40%. By targeting only LPs that are actively deploying, the manager avoids LPs that are paused or out of market.
• Increase conversion rates by 25%. By timing outreach to coincide with an LP's active period, the manager increases the likelihood of a meeting.
• Shorten fundraising cycles by 30%. By focusing on LPs that are ready to commit, the manager closes funds faster.

Example: A $75 million growth equity fund raised Fund II in 2026. The team used Altss for signal intelligence and targeted 150 LPs that were actively deploying. They closed $60 million in 8 months. The same team had raised Fund I in 2024 using a legacy database, targeting 400 LPs and closing $50 million in 14 months.

The Global LP Landscape in 2026: Where the Capital Is#

Understanding where capital is flowing is critical for fund managers. Here is a breakdown of the global LP landscape in 2026.

North America#

North America remains the largest LP market, with $12 trillion in private equity and venture capital allocations. Key trends:

• U.S. pension funds are increasing allocations to private markets. CalPERS, CalSTRS, and the New York State Common Retirement Fund have all announced increases in 2025–2026.
• Endowments and foundations are shifting toward direct investments and co-investments. The Yale Endowment, for example, now allocates 35% of its portfolio to private equity.
• Family offices are growing rapidly. There are 3,200+ family offices in North America, with $1.5 trillion in assets under management.

Example: The $400 billion California Public Employees' Retirement System (CalPERS) announced a $2.5 billion increase to its private equity allocation in Q1 2026. Fund managers who tracked this signal within 30 days had a first-mover advantage.

Europe#

Europe is the second-largest LP market, with $8 trillion in private markets allocations. Key trends:

• Sovereign wealth funds in Norway, the Netherlands, and Switzerland are increasing allocations to venture capital and growth equity.
• Pension funds in the UK and Netherlands are consolidating, creating larger pools of capital.
• Family offices in Switzerland, Germany, and the UK are becoming more active in direct investing.

Example: The €1.4 trillion Norwegian Government Pension Fund Global (GPFG) increased its private equity allocation to 5% in 2025, up from 3%. Fund managers targeting GPFG needed to understand the new mandate.

Asia#

Asia is the fastest-growing LP market, with $5 trillion in private markets allocations. Key trends:

• Sovereign wealth funds in China, Singapore, and the Middle East are major allocators. The China Investment Corporation (CIC) and Temasek are increasing allocations to venture capital.
• Family offices in Singapore and Hong Kong are growing rapidly. There are 1,800+ family offices in Asia, with $800 billion in assets under management.
• Pension funds in Japan and South Korea are increasing allocations to private markets.

Example: The $800 billion China Investment Corporation (CIC) announced a $1 billion commitment to global venture capital in 2025. Fund managers targeting CIC needed to navigate complex regulatory and relationship dynamics.

Middle East and Africa#

The Middle East is a significant LP market, with $3 trillion in private markets allocations. Key trends:

• Sovereign wealth funds in the UAE, Saudi Arabia, and Qatar are major allocators. The Abu Dhabi Investment Authority (ADIA) and the Public Investment Fund (PIF) are increasing allocations to technology and healthcare.
• Family offices in the UAE and Saudi Arabia are becoming more active in venture capital.
• Africa is an emerging market, with $500 billion in private markets allocations. Pension funds in South Africa and Nigeria are increasing allocations.

Example: The $700 billion Public Investment Fund (PIF) of Saudi Arabia announced a $500 million commitment to global venture capital in 2025. Fund managers targeting PIF needed to understand the fund's focus on technology and sustainability.

Tactical Advice for Fund Managers and Emerging GPs#

1. Prioritize Freshness Over Breadth#

A database with 100,000 LPs updated annually is less valuable than a database with 10,000 LPs updated monthly. Focus on platforms with sub-30-day refresh cycles for LP data.

Action: Evaluate each platform's refresh cycle. Ask for a sample of recent updates to verify timeliness.

2. Use Signal Intelligence to Time Outreach#

Do not send pitches to LPs that are not actively deploying. Use signal intelligence to identify LPs that are fundraising, recently hired, or attending relevant events.

Action: Set up alerts for specific signals: new hires, mandate changes, event attendance. Prioritize LPs with recent signals.

3. Build Relationship Context Before Outreach#

A warm introduction is 5x more likely to result in a meeting than a cold email. Use relationship mapping to identify shared portfolio companies, co-investors, and mutual connections.

Action: Before reaching out to an LP, check for warm paths. If none exist, consider attending events where the LP is present or asking for an introduction from a mutual connection.

4. Target Family Offices with Signal-Based Detection#

Family offices are a growing source of capital, but they are difficult to track. Use signal-based detection to identify family offices that are actively deploying.

Action: Look for family offices that have recently hired investment professionals, attended relevant conferences, or co-invested in deals. These are likely active allocators.

5. Diversify Your Platform Stack#

No single platform covers everything. Use a combination of tools for different workflows:

• Altss for fundraising velocity, signal intelligence, and relationship context.
• PitchBook for market research and company data.
• Preqin for institutional benchmarking.
• Dakota for U.S. institutional execution in Salesforce.
• FINTRX for family office research.

Action: Allocate budget across 2–3 platforms. For most fund managers, Altss + PitchBook is a strong combination.

6. Verify Contacts Before Outreach#

Do not assume that a database's contact data is accurate. Verify decision-maker names, email addresses, and phone numbers before sending a pitch.

Action: Use tools like Hunter, Apollo, or Lusha to verify email addresses. Cross-reference with LinkedIn and company websites.

7. Track Your Outreach Metrics#

Measure the effectiveness of your outreach. Track metrics such as:

• Open rate: Percentage of emails opened.
• Reply rate: Percentage of emails that receive a response.
• Meeting rate: Percentage of emails that result in a meeting.
• Conversion rate: Percentage of meetings that result in a commitment.

Action: Use a CRM to track these metrics. Compare performance across platforms and adjust your strategy accordingly.

8. Attend the Right Events#

Events are a powerful way to meet LPs. Focus on events where your target LPs are present.

Action: Use Altss to identify which events your target LPs are attending. Prioritize events with high LP density.

9. Build a Long-Term Relationship Pipeline#

Fundraising is not a one-time event. Build relationships with LPs over time, even if they are not ready to commit today.

Action: Create a pipeline of LPs at different stages: awareness, interest, evaluation, commitment. Nurture relationships through regular updates and check-ins.

10. Use Data to Tell a Story#

LPs receive hundreds of pitches per year. Use data to differentiate your fund.

Action: Include specific data points in your pitch: market size, growth rate, comparable fund performance, LP demand. Use platforms like PitchBook and Preqin to benchmark your fund against peers.

Case Studies: Real Fundraising Campaigns in 2026#

Case Study 1: Emerging Manager Raises Fund I Using Signal Intelligence#

Fund: $50 million early-stage VC fund

Target LPs: Family offices and high-net-worth individuals

Platform Used: Altss

Challenge: The fund was raising Fund I with no track record. The team needed to identify family offices that were actively deploying into early-stage venture capital.

Approach:

• Used Altss to identify 150 family offices with recent signals: new hires, event attendance, and portfolio updates.
• Prioritized 50 family offices that had co-invested in at least two deals in the past six months.
• Used relationship mapping to identify warm paths: shared portfolio companies and mutual connections.
• Sent personalized pitches referencing the family office's recent activity.

Results:

• 40 meetings scheduled (80% meeting rate from prioritized list)
• 12 commitments totaling $35 million (70% of target)
• Fund closed in 8 months

Key Takeaway: Signal intelligence and relationship context enabled the team to focus on LPs that were ready to deploy. The team avoided 100+ LPs that were not active.

Case Study 2: Institutional Fund Raises Fund II Using Multi-Platform Stack#

Fund: $200 million growth equity fund

Target LPs: U.S. pension funds, endowments, and foundations

Platforms Used: Altss, PitchBook, Dakota

Challenge: The fund had a strong track record from Fund I but needed to expand its LP base. The team targeted 200 institutional LPs across the U.S.

Approach:

• Used PitchBook to benchmark the fund's performance against peers and identify comparable funds.
• Used Dakota to manage the pipeline in Salesforce and track meetings.
• Used Altss to identify signal intelligence: which LPs were actively deploying and which had recently hired new investment professionals.

Results:

• 150 meetings scheduled (75% meeting rate from targeted list)
• 25 commitments totaling $180 million (90% of target)
• Fund closed in 10 months

Key Takeaway: The multi-platform stack allowed the team to leverage each platform's strengths. PitchBook for research, Dakota for execution